{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "RC Car End-to-End Image Regression with CNNs (Lidar).ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/wilselby/diy_driverless_car_ROS/blob/ml-model/rover_ml/colab/RC_Car_End_to_End_Image_Regression_with_CNNs_(Lidar).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Ob-YPAvx7Nr",
        "colab_type": "text"
      },
      "source": [
        "# Development of an End-to-End ML Model for Navigating an RC car with a Lidar"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uPcv0EFav6LH",
        "colab_type": "text"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/wilselby/diy_driverless_car_ROS/blob/ml-model/RC_Car_End_to_End_Image_Regression_with_CNNs_(RGB_camera).ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />\n",
        "    Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/wilselby/diy_driverless_car_ROS/blob/ml-model/rover_ml/colab/RC_Car_End_to_End_Image_Regression_with_CNNs_(RGB_camera).ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />\n",
        "    View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EPxyR5SargY9",
        "colab_type": "text"
      },
      "source": [
        "#Environment Setup\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZbZCuOD2JLH",
        "colab_type": "text"
      },
      "source": [
        "## Import Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqUpKnu52L5S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "78702cb6-468a-4973-85fa-71f9fa9e1a2f"
      },
      "source": [
        "import os\n",
        "import csv\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "import pprint\n",
        "\n",
        "import numpy as np\n",
        "from numpy import expand_dims\n",
        "\n",
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "\n",
        "from keras import backend as K\n",
        "from keras.models import Model, Sequential\n",
        "from keras.models import load_model\n",
        "from keras.layers import Dense, GlobalAveragePooling2D, MaxPooling2D, Lambda, Cropping2D\n",
        "from keras.layers.convolutional import Convolution2D\n",
        "from keras.layers.core import Flatten, Dense, Dropout, SpatialDropout2D\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import ModelCheckpoint, TensorBoard\n",
        "from keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.preprocessing.image import load_img\n",
        "from keras.preprocessing.image import img_to_array \n",
        "   \n",
        "from google.colab.patches import cv2_imshow\n",
        "  \n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "import pandas as pd\n",
        "\n",
        "print(\"Tensorflow Version:\",tf.__version__)\n",
        "print(\"Tensorflow Keras Version:\",tf.keras.__version__)\n",
        "print(\"Eager mode: \", tf.executing_eagerly())\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "('Tensorflow Version:', '1.15.0')\n",
            "('Tensorflow Keras Version:', '2.2.4-tf')\n",
            "('Eager mode: ', False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vy5iiwR19nJ",
        "colab_type": "text"
      },
      "source": [
        "## Confirm TensorFlow can see the GPU \n",
        "\n",
        "Simply select \"GPU\" in the Accelerator drop-down in Notebook Settings (either through the Edit menu or the command palette at cmd/ctrl-shift-P)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_0h6Afcy2E9l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5bff8a6d-39c7-49a1-8841-27344562bc5f"
      },
      "source": [
        "device_name = tf.test.gpu_device_name()\n",
        "\n",
        "if device_name != '/device:GPU:0':\n",
        "  # Raise SystemError('GPU device not found')\n",
        "  print('GPU device not found')\n",
        "else:\n",
        "  print('Found GPU at: {}'.format(device_name))\n",
        "  \n",
        "  # GPU count and name\n",
        "  !nvidia-smi -L"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n",
            "GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-47aa6e31-82c5-f73d-7d78-373fb85348e8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2sPUxOR3FlN",
        "colab_type": "text"
      },
      "source": [
        "# Load the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YsJN12w3aug",
        "colab_type": "text"
      },
      "source": [
        "## Download and Extract the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfRnrsuuRXBL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "d0d96695-c476-4968-a934-2ba6462016d2"
      },
      "source": [
        "# Download the dataset\n",
        "!curl -O https://selbystorage.s3-us-west-2.amazonaws.com/research/office_3/office_3.tar.gz"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  257M  100  257M    0     0  23.9M      0  0:00:10  0:00:10 --:--:-- 54.1M\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HV6z9-T4hd-G",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "abd4a3ea-97b1-4bbd-a4b2-c0df6d33d601"
      },
      "source": [
        "data_set = 'office_3'\n",
        "tar_file = data_set + '.tar.gz'\n",
        "\n",
        "# Unzip the .tgz file\n",
        "# -x for extract\n",
        "# -v for verbose \n",
        "# -z for gnuzip\n",
        "# -f for file (should come at last just before file name)\n",
        "# -C to extract the zipped contents to a different directory\n",
        "!tar -xvzf $tar_file"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "office_3/\n",
            "office_3/ml_training_2019-12-26-14-59-40_12.yaml\n",
            "office_3/ml_training_2019-12-28-10-32-38_7.yaml\n",
            "office_3/camera.csv\n",
            "office_3/ml_training_2019-12-26-14-54-02_0.yaml\n",
            "office_3/ml_training_2019-12-28-10-31-15_5.yaml\n",
            "office_3/ml_training_2019-12-26-14-54-58_2.yaml\n",
            "office_3/ml_training_2019-12-26-14-59-11_11.yaml\n",
            "office_3/ml_training_2019-12-28-10-30-32_4.yaml\n",
            "office_3/ml_training_2019-12-26-14-55-25_3.yaml\n",
            "office_3/.~lock.interpolated.csv#\n",
            "office_3/ml_training_2019-12-26-14-58-43_10.yaml\n",
            "office_3/ml_training_2019-12-26-14-57-46_8.yaml\n",
            "office_3/ml_training_2019-12-26-14-56-50_6.yaml\n",
            "office_3/ml_training_2019-12-28-10-27-15_0.yaml\n",
            "office_3/interpolated.csv\n",
            "office_3/steering.csv\n",
            "office_3/ml_training_2019-12-26-14-57-18_7.yaml\n",
            "office_3/ml_training_2019-12-28-10-29-24_3.yaml\n",
            "office_3/ml_training_2019-12-26-14-54-30_1.yaml\n",
            "office_3/ml_training_2019-12-28-10-31-56_6.yaml\n",
            "office_3/ml_training_2019-12-28-10-28-42_2.yaml\n",
            "office_3/ml_training_2019-12-28-10-28-00_1.yaml\n",
            "office_3/ml_training_2019-12-26-14-58-15_9.yaml\n",
            "office_3/center/\n",
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            "office_3/center/1577401175382619648.jpg\n",
            "office_3/ml_training_2019-12-26-14-55-53_4.yaml\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9FogCRzr3sUF",
        "colab_type": "text"
      },
      "source": [
        "## Parse the CSV File"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KeQ8c-9s3v8y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "2809ba3d-799a-41db-b850-0579f2b2919f"
      },
      "source": [
        "# Define path to csv file\n",
        "csv_path = data_set + '/interpolated.csv'\n",
        "\n",
        "# Load the CSV file into a pandas dataframe\n",
        "df = pd.read_csv(csv_path, sep=\",\")\n",
        "\n",
        "# Print the dimensions\n",
        "print(\"Dataset Dimensions:\")\n",
        "print(df.shape)\n",
        "\n",
        "# Print the first 5 lines of the dataframe for review\n",
        "print(\"\\nDataset Summary:\")\n",
        "df.head(5)\n",
        "  "
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset Dimensions:\n",
            "(6253, 8)\n",
            "\n",
            "Dataset Summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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              "                           index            timestamp  ...  angle  speed\n",
              "0  2019-12-26 22:54:03.912922880  1577400843912922880  ...    0.0    0.0\n",
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              "3  2019-12-26 22:54:04.063207680  1577400844063207680  ...    0.0    0.0\n",
              "4  2019-12-26 22:54:04.113175552  1577400844113175552  ...    0.0    0.0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3BnBpo6-F2oR",
        "colab_type": "text"
      },
      "source": [
        "# Clean and Pre-process the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ub8CkShSJEkV",
        "colab_type": "text"
      },
      "source": [
        "## Remove Unneccessary Columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDth_K-3JINP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "ca848252-0a52-467e-8695-915ed537df60"
      },
      "source": [
        "# Remove 'index' and 'frame_id' columns \n",
        "df.drop(['index','frame_id'],axis=1,inplace=True)\n",
        "\n",
        "# Verify new dataframe dimensions\n",
        "print(\"Dataset Dimensions:\")\n",
        "print(df.shape)\n",
        "\n",
        "# Print the first 5 lines of the new dataframe for review\n",
        "print(\"\\nDataset Summary:\")\n",
        "df.head(5)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Dataset Dimensions:\n",
            "(6253, 6)\n",
            "\n",
            "Dataset Summary:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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              "             timestamp  width  ...  angle speed\n",
              "0  1577400843912922880   1024  ...    0.0   0.0\n",
              "1  1577400843963078912   1024  ...    0.0   0.0\n",
              "2  1577400844013053184   1024  ...    0.0   0.0\n",
              "3  1577400844063207680   1024  ...    0.0   0.0\n",
              "4  1577400844113175552   1024  ...    0.0   0.0\n",
              "\n",
              "[5 rows x 6 columns]"
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-zof8fUDz2C",
        "colab_type": "text"
      },
      "source": [
        "## Detect Missing Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ffOXGmmQD2om",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "88556d85-dac4-4fdf-e2c3-025177640d85"
      },
      "source": [
        "# Detect Missing Values\n",
        "print(\"Any Missing Values?: {}\".format(df.isnull().values.any()))\n",
        "\n",
        "# Total Sum\n",
        "print(\"\\nTotal Number of Missing Values: {}\".format(df.isnull().sum().sum()))\n",
        "\n",
        "# Sum Per Column\n",
        "print(\"\\nTotal Number of Missing Values per Column:\")\n",
        "print(df.isnull().sum())"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Any Missing Values?: False\n",
            "\n",
            "Total Number of Missing Values: 0\n",
            "\n",
            "Total Number of Missing Values per Column:\n",
            "timestamp    0\n",
            "width        0\n",
            "height       0\n",
            "filename     0\n",
            "angle        0\n",
            "speed        0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKBJ4sODIFOC",
        "colab_type": "text"
      },
      "source": [
        "## Remove Zero Throttle Values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QAk-fsbkIJrh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "7cfefbcd-74d2-445b-b462-8db1cbf9ffea"
      },
      "source": [
        "# Determine if any throttle values are zeroes\n",
        "print(\"Any 0 throttle values?: {}\".format(df['speed'].eq(0).any()))\n",
        "\n",
        "# Determine number of 0 throttle values:\n",
        "print(\"\\nNumber of 0 throttle values: {}\".format(df['speed'].eq(0).sum()))\n",
        "\n",
        "# Remove rows with 0 throttle values\n",
        "if df['speed'].eq(0).any():\n",
        "  df = df.query('speed != 0')\n",
        "  \n",
        "  # Reset the index\n",
        "  df.reset_index(inplace=True,drop=True)\n",
        "  \n",
        "# Verify new dataframe dimensions\n",
        "print(\"\\nNew Dataset Dimensions:\")\n",
        "print(df.shape)\n",
        "df.head(5)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Any 0 throttle values?: True\n",
            "\n",
            "Number of 0 throttle values: 55\n",
            "\n",
            "New Dataset Dimensions:\n",
            "(6198, 6)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1577400844263394816</td>\n",
              "      <td>1024</td>\n",
              "      <td>64</td>\n",
              "      <td>center/1577400844263394816.jpg</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.002261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1577400844313359616</td>\n",
              "      <td>1024</td>\n",
              "      <td>64</td>\n",
              "      <td>center/1577400844313359616.jpg</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.006871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1577400844363330560</td>\n",
              "      <td>1024</td>\n",
              "      <td>64</td>\n",
              "      <td>center/1577400844363330560.jpg</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.011482</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1577400844413312256</td>\n",
              "      <td>1024</td>\n",
              "      <td>64</td>\n",
              "      <td>center/1577400844413312256.jpg</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.016252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1577400844463289088</td>\n",
              "      <td>1024</td>\n",
              "      <td>64</td>\n",
              "      <td>center/1577400844463289088.jpg</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.021229</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             timestamp  width  ...  angle     speed\n",
              "0  1577400844263394816   1024  ...    0.0  0.002261\n",
              "1  1577400844313359616   1024  ...    0.0  0.006871\n",
              "2  1577400844363330560   1024  ...    0.0  0.011482\n",
              "3  1577400844413312256   1024  ...    0.0  0.016252\n",
              "4  1577400844463289088   1024  ...    0.0  0.021229\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6XZJLwqKCbE7",
        "colab_type": "text"
      },
      "source": [
        "## View Label Statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgOG94fnCeDB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "bd00a11f-7e0d-4cd8-fa3e-54ee2fb7ff58"
      },
      "source": [
        "# Steering Command Statistics\n",
        "print(\"\\nSteering Command Statistics:\")\n",
        "print(df['angle'].describe())\n",
        "\n",
        "print(\"\\nThrottle Command Statistics:\")\n",
        "# Throttle Command Statistics\n",
        "print(df['speed'].describe())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Steering Command Statistics:\n",
            "count    6198.000000\n",
            "mean        0.822324\n",
            "std         1.008619\n",
            "min        -1.533349\n",
            "25%         0.471148\n",
            "50%         1.380756\n",
            "75%         1.466080\n",
            "max         1.531508\n",
            "Name: angle, dtype: float64\n",
            "\n",
            "Throttle Command Statistics:\n",
            "count    6198.000000\n",
            "mean        0.129354\n",
            "std         0.015255\n",
            "min         0.002261\n",
            "25%         0.118947\n",
            "50%         0.131835\n",
            "75%         0.139840\n",
            "max         0.150000\n",
            "Name: speed, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nae5TmmFFJ5T",
        "colab_type": "text"
      },
      "source": [
        "## View Histogram of Steering Commands"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bh3DSasZQKCi",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "outputId": "a196cdf2-feab-48c2-9396-72f67f8f6f59"
      },
      "source": [
        "#@title Select the number of histogram bins\n",
        "\n",
        "num_bins = 25 #@param {type:\"slider\", min:5, max:50, step:1}\n",
        "\n",
        "hist, bins = np.histogram(df['angle'], num_bins)\n",
        "center = (bins[:-1]+ bins[1:]) * 0.5\n",
        "plt.bar(center, hist, width=0.05)\n",
        "#plt.plot((np.min(df['angle']), np.max(df['angle'])), (samples_per_bin, samples_per_bin))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 25 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rPma2AKrq4apa7JfGlpUhx/Lby2pU1f8ABy+rsdxsALa26a3ApRPsy2IM8zoPjvFLwPlJ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwunrGrDQweC",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "# Normalize the histogram (150-300 for RBG)\n",
        "#@title Normalize the Histogram { run: \"auto\" }\n",
        "hist = False #@param {type:\"boolean\"}\n",
        "\n",
        "remove_list = []\n",
        "samples_per_bin = 150\n",
        "\n",
        "if hist:\n",
        "  for j in range(num_bins):\n",
        "    list_ = []\n",
        "    for i in range(len(df['angle'])):\n",
        "      if df.loc[i,'angle'] >= bins[j] and df.loc[i,'angle'] <= bins[j+1]:\n",
        "        list_.append(i)\n",
        "    random.shuffle(list_)\n",
        "    list_ = list_[samples_per_bin:]\n",
        "    remove_list.extend(list_)\n",
        "\n",
        "  print('removed:', len(remove_list))\n",
        "  df.drop(df.index[remove_list], inplace=True)\n",
        "  df.reset_index(inplace=True)\n",
        "  df.drop(['index'],axis=1,inplace=True)\n",
        "  print('remaining:', len(df))\n",
        "  \n",
        "  hist, _ = np.histogram(df['angle'], (num_bins))\n",
        "  plt.bar(center, hist, width=0.05)\n",
        "  plt.plot((np.min(df['angle']), np.max(df['angle'])), (samples_per_bin, samples_per_bin))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1uqfxZ4uGoNX",
        "colab_type": "text"
      },
      "source": [
        "## View a Sample Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L2nwHnC4Gq1m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "outputId": "aae1b424-85b9-47dd-cd2c-ff4f222e2930"
      },
      "source": [
        "# View a Single Image \n",
        "index = random.randint(0,df.shape[0]-1)\n",
        "\n",
        "img_name = data_set + '/' + df.loc[index,'filename']\n",
        "angle = df.loc[index,'angle']\n",
        "\n",
        "center_image = cv2.imread(img_name)\n",
        "center_image_mod = cv2.resize(center_image, (320,180))\n",
        "center_image_mod = cv2.cvtColor(center_image_mod,cv2.COLOR_RGB2BGR)\n",
        "\n",
        "# Crop the image\n",
        "height_min = 100 \n",
        "height_max = 170\n",
        "width_min = 120\n",
        "width_max = 200\n",
        "bottom_crop = center_image_mod.shape[0] - height_max\n",
        "right_crop = center_image_mod.shape[1] - width_max\n",
        "\n",
        "crop_img = center_image_mod[height_min:height_max, width_min:width_max]\n",
        "\n",
        "plt.subplot(2,1,1)\n",
        "plt.imshow(center_image_mod)\n",
        "plt.grid(False)\n",
        "plt.xlabel('angle: {:.2}'.format(angle))\n",
        "plt.show() \n",
        "\n",
        "plt.subplot(2,1,2)\n",
        "plt.imshow(crop_img)\n",
        "plt.grid(False)\n",
        "plt.xlabel('angle: {:.2}'.format(angle))\n",
        "plt.show() "
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VS5cuxbPPPosZM2ZkOfJypz+dTkOhUGSZTKIoYsqUKfB6vejs7MRDDz2ESZMmoaamBrfc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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QzFgx7QjFjPUmyDl30Dn3ixs4/1NDlId3zs1c5bjeEP3wvHPutor7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\nQv8f005/0qy/BlMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTQywtOyGvLv",
        "colab_type": "text"
      },
      "source": [
        "## View Multiple Images"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ODmdWWpsGxK2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "907ed9d3-b136-48b2-c67d-ec91f9525614"
      },
      "source": [
        "# Number of Images to Display\n",
        "num_images = 4\n",
        "\n",
        "# Display the images\n",
        "i = 0\n",
        "for i in range (i,num_images):\n",
        "    index = random.randint(0,df.shape[0]-1)\n",
        "    image_path = df.loc[index,'filename']\n",
        "    angle = df.loc[index,'angle']\n",
        "    img_name = data_set + '/' + image_path\n",
        "    image = cv2.imread(img_name)\n",
        "    image = cv2.resize(image, (320,180))\n",
        "    image = cv2.cvtColor(image,cv2.COLOR_RGB2BGR)\n",
        "    plt.subplot(num_images/2,num_images/2,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(image, cmap=plt.cm.binary)\n",
        "    plt.xlabel('angle: {:.3}'.format(angle))\n",
        "    i += 1"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MmVKpRGtrKzsPL7roIlitVhauUosgn8/zoqXNQyooaL7o70hIEASUlZXxPAEDlpDJZMKY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\n1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cjE43ERwnc4HNi3bx/OO+88DA4OCgWPtNLBwUFs2rQJL7/8MiKRiIi8m81mkUnQ69Pj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BQUEBZsyYIV2nVVXF97//fbz22muTfn48HpfKxb/85S/4/ve/fwxWnG5AtFEG9boZJe3atUvY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\n0m4RSzrkpCUtaUm7Rez/A//UnGdYkzCIAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BgFvPAZl9vfP",
        "colab_type": "text"
      },
      "source": [
        "# Split the Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clqhrZpXtYQy",
        "colab_type": "text"
      },
      "source": [
        "## Define an ImageDataGenerator to Augment Images\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FcAb3NLiten6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create image data augmentation generator and choose augmentation types\n",
        "datagen = ImageDataGenerator(\n",
        "                             #rotation_range=20,\n",
        "                             zoom_range=0.15,\n",
        "                             #width_shift_range=0.1,\n",
        "                             #height_shift_range=0.2,\n",
        "                             #shear_range=10,\n",
        "                             brightness_range=[0.5,1.0],\n",
        "                          \t #horizontal_flip=True,\n",
        "                             #vertical_flip=True,\n",
        "                             #channel_shift_range=100.0,\n",
        "                             fill_mode=\"reflect\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0iJWp1YE-u5",
        "colab_type": "text"
      },
      "source": [
        "## View Image Augmentation Examples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxKea6xhVO2T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "outputId": "1ee7807c-a37b-4923-c62f-a41bc2e2eb68"
      },
      "source": [
        "# load the image\n",
        "index = random.randint(0,df.shape[0]-1)\n",
        "\n",
        "img_name = data_set + '/' + df.loc[index,'filename']\n",
        "original_image = cv2.imread(img_name)\n",
        "original_image = cv2.cvtColor(original_image,cv2.COLOR_RGB2BGR)\n",
        "original_image = cv2.resize(original_image, (320,180))\n",
        "label = df.loc[index,'angle']\n",
        "\n",
        "# convert to numpy array\n",
        "data = img_to_array(original_image)\n",
        "\n",
        "# expand dimension to one sample\n",
        "test = expand_dims(data, 0)\n",
        "\n",
        "# prepare iterator\n",
        "it = datagen.flow(test, batch_size=1)\n",
        "\n",
        "# generate batch of images\n",
        "batch = it.next()\n",
        "\n",
        "# convert to unsigned integers for viewing\n",
        "image_aug = batch[0].astype('uint8')\n",
        "\n",
        "print(\"Augmenting a Single Image: \\n\")\n",
        "\n",
        "plt.subplot(2,1,1)\n",
        "plt.imshow(original_image)\n",
        "plt.grid(False)\n",
        "plt.xlabel('angle: {:.2}'.format(label))\n",
        "plt.show() \n",
        "\n",
        "plt.subplot(2,1,2)\n",
        "plt.imshow(image_aug)\n",
        "plt.grid(False)\n",
        "plt.xlabel('angle: {:.2}'.format(label))\n",
        "plt.show() \n",
        "\n",
        "print(\"Multiple Augmentations: \\n\")\n",
        "# generate samples and plot\n",
        "for i in range(0,num_images):\n",
        "\t# define subplot\n",
        "\tplt.subplot(num_images/2,num_images/2,i+1)\n",
        "\t# generate batch of images\n",
        "\tbatch = it.next()\n",
        "\t# convert to unsigned integers for viewing\n",
        "\timage = batch[0].astype('uint8')\n",
        "\t# plot raw pixel data\n",
        "\tplt.imshow(image)\n",
        "# show the figure\n",
        "plt.show()\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Augmenting a Single Image: \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GUwDft7a2IpvNsg/9/vvvY//+/di5c2fBOZWASy6Xg8vlQi6Xm9SuU45UVVVx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\nNtuWyf4fB39iHt+pV4EAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tZoFwUft8PkxNTXVtjdCD3mg0kM/nUSqVJCOBg5dOp7GwsIBQKCTVjQHIjm2W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\nDekB0ZDZhjSkB0RDZhvSkB4Q/f956GcqhR37PgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Multiple Augmentations: \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8/Lw4LA8dOoTe3t5VuLQKt1CwM6aZB1GlUsHFixdRLBaxtLQkmO7C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RjtdjvMZjNMJpMYBZPJBLPZDKfTCZfLBbvdDovFgkajAbvdDrfbDbfbDY/HA7vdLtfg9wGAw+GA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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUE19jPetzBs",
        "colab_type": "text"
      },
      "source": [
        "## Define a Data Generator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TgtIMpoSt1LW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generator(samples, batch_size=32, aug=0):\n",
        "    num_samples = len(samples)\n",
        "\n",
        "    while 1:  # Loop forever so the generator never terminates\n",
        "        for offset in range(0, num_samples, batch_size):\n",
        "            batch_samples = samples[offset:offset + batch_size]\n",
        "\n",
        "            #print(batch_samples)\n",
        "            images = []\n",
        "            angles = []\n",
        "            for batch_sample in batch_samples:\n",
        "                if batch_sample[5] != \"filename\":\n",
        "                    name = data_set + '/' + batch_sample[3]\n",
        "                    center_image = cv2.imread(name)\n",
        "                    center_image = cv2.cvtColor(center_image,cv2.COLOR_RGB2BGR)\n",
        "                    center_image = cv2.resize(\n",
        "                        center_image,\n",
        "                        (320, 180))  #resize from 720x1280 to 180x320\n",
        "                    angle = float(batch_sample[4])\n",
        "                    if not aug:\n",
        "                      images.append(center_image)\n",
        "                      angles.append(angle)\n",
        "                    else:\n",
        "                        data = img_to_array(center_image)\n",
        "                        sample = expand_dims(data, 0)\n",
        "                        it = datagen.flow(sample, batch_size=1)\n",
        "                        batch = it.next()\n",
        "                        image_aug = batch[0].astype('uint8')\n",
        "                        if random.random() < .5:\n",
        "                          image_aug = np.fliplr(image_aug)\n",
        "                          angle = -1 * angle\n",
        "                        images.append(image_aug)\n",
        "                        angles.append(angle)\n",
        "\n",
        "            X_train = np.array(images)\n",
        "            y_train = np.array(angles)\n",
        "\n",
        "            yield sklearn.utils.shuffle(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PAVmOpT8HEg0",
        "colab_type": "text"
      },
      "source": [
        "## Split the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZWDPKBnvGZI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "507fc6eb-ca7d-488d-db4d-343bbbba833f"
      },
      "source": [
        "samples = []\n",
        "\n",
        "samples = df.values.tolist()\n",
        "\n",
        "sklearn.utils.shuffle(samples)\n",
        "train_samples, validation_samples = train_test_split(samples, test_size=0.2)\n",
        "\n",
        "print(\"Number of traing samples: \", len(train_samples))\n",
        "print(\"Number of validation samples: \", len(validation_samples))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('Number of traing samples: ', 4958)\n",
            "('Number of validation samples: ', 1240)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g5u_LimyvOkv",
        "colab_type": "text"
      },
      "source": [
        "## Define Training and Validation Data Generators"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ux8mS7YRpQaX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size_value = 32\n",
        "img_aug = 1\n",
        "\n",
        "train_generator = generator(train_samples, batch_size=batch_size_value, aug=img_aug)\n",
        "validation_generator = generator(\n",
        "    validation_samples, batch_size=batch_size_value, aug=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_WW4C27_4HO",
        "colab_type": "text"
      },
      "source": [
        "# Compile and Train the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z62Wkaj4ADbB",
        "colab_type": "text"
      },
      "source": [
        "## Build the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AK578kaYAE1_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "68585dc7-fa1c-4a7e-fdfd-0b502b9d0ddb"
      },
      "source": [
        "# Initialize the model\n",
        "model = Sequential()\n",
        "\n",
        "# trim image to only see section with road\n",
        "# (top_crop, bottom_crop), (left_crop, right_crop)\n",
        "model.add(Cropping2D(cropping=((height_min,bottom_crop), (width_min,right_crop)), input_shape=(180,320,3)))\n",
        "\n",
        "# Preprocess incoming data, centered around zero with small standard deviation\n",
        "model.add(Lambda(lambda x: (x / 255.0) - 0.5))\n",
        "\n",
        "# Nvidia model\n",
        "model.add(Convolution2D(24, (5, 5), activation=\"relu\", name=\"conv_1\", strides=(2, 2)))\n",
        "model.add(Convolution2D(36, (5, 5), activation=\"relu\", name=\"conv_2\", strides=(2, 2)))\n",
        "model.add(Convolution2D(48, (5, 5), activation=\"relu\", name=\"conv_3\", strides=(2, 2)))\n",
        "model.add(SpatialDropout2D(.5, dim_ordering='default'))\n",
        "\n",
        "model.add(Convolution2D(64, (3, 3), activation=\"relu\", name=\"conv_4\", strides=(1, 1)))\n",
        "model.add(Convolution2D(64, (3, 3), activation=\"relu\", name=\"conv_5\", strides=(1, 1)))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(1164))\n",
        "model.add(Dropout(.5))\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dropout(.5))\n",
        "model.add(Dense(50, activation='relu'))\n",
        "model.add(Dropout(.5))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dropout(.5))\n",
        "model.add(Dense(1))\n",
        "\n",
        "model.compile(loss='mse', optimizer=Adam(lr=0.001), metrics=['mse','mae','mape','cosine'])\n",
        "\n",
        "# Print model sumamry\n",
        "model.summary()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:14: UserWarning: Update your `SpatialDropout2D` call to the Keras 2 API: `SpatialDropout2D(0.5, data_format=None)`\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "cropping2d_3 (Cropping2D)    (None, 70, 80, 3)         0         \n",
            "_________________________________________________________________\n",
            "lambda_3 (Lambda)            (None, 70, 80, 3)         0         \n",
            "_________________________________________________________________\n",
            "conv_1 (Conv2D)              (None, 33, 38, 24)        1824      \n",
            "_________________________________________________________________\n",
            "conv_2 (Conv2D)              (None, 15, 17, 36)        21636     \n",
            "_________________________________________________________________\n",
            "conv_3 (Conv2D)              (None, 6, 7, 48)          43248     \n",
            "_________________________________________________________________\n",
            "spatial_dropout2d_3 (Spatial (None, 6, 7, 48)          0         \n",
            "_________________________________________________________________\n",
            "conv_4 (Conv2D)              (None, 4, 5, 64)          27712     \n",
            "_________________________________________________________________\n",
            "conv_5 (Conv2D)              (None, 2, 3, 64)          36928     \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 384)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 1164)              448140    \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 1164)              0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 100)               116500    \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 50)                5050      \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 10)                510       \n",
            "_________________________________________________________________\n",
            "dropout_12 (Dropout)         (None, 10)                0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 1)                 11        \n",
            "=================================================================\n",
            "Total params: 701,559\n",
            "Trainable params: 701,559\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "USKQYVmhAMaJ",
        "colab_type": "text"
      },
      "source": [
        "## Setup Checkpoints"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BS6R3FVoAOPc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# checkpoint\n",
        "model_path = './model'\n",
        "\n",
        "!if [ -d $model_path ]; then echo 'Directory Exists'; else mkdir $model_path; fi\n",
        "\n",
        "filepath = model_path + \"/weights-improvement-{epoch:02d}-{val_loss:.2f}.hdf5\"\n",
        "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='auto', period=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImbsdKhSOKw3",
        "colab_type": "text"
      },
      "source": [
        "## Setup Early Stopping to Prevent Overfitting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rhi1wb2yOLvY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# The patience parameter is the amount of epochs to check for improvement\n",
        "early_stop = EarlyStopping(monitor='val_loss', patience=10)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDwl4ZB9boi2",
        "colab_type": "text"
      },
      "source": [
        "## Reduce Learning Rate When a Metric has Stopped Improving"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1xTmpV-bywp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,\n",
        "                              patience=5, min_lr=0.001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVIGONgjSy7V",
        "colab_type": "text"
      },
      "source": [
        "## Setup Tensorboard"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YWTAEeyS0tw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "0305f7df-2565-461e-ddfd-1353cbf1adad"
      },
      "source": [
        "# Clear any logs from previous runs\n",
        "!rm -rf ./Graph/ \n",
        "\n",
        "# Launch Tensorboard\n",
        "!pip install -U tensorboardcolab\n",
        "\n",
        "from tensorboardcolab import *\n",
        "\n",
        "tbc = TensorBoardColab()\n",
        "\n",
        "# Configure the Tensorboard Callback\n",
        "tbCallBack = TensorBoard(log_dir='./Graph', \n",
        "                        histogram_freq=1,\n",
        "                        write_graph=True,\n",
        "                        write_grads=True,\n",
        "                        write_images=True,\n",
        "                        batch_size=batch_size_value,\n",
        "                        update_freq='epoch')\n"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: tensorboardcolab in /usr/local/lib/python2.7/dist-packages (0.0.22)\n",
            "Wait for 8 seconds...\n",
            "TensorBoard link:\n",
            "http://5deaa30c.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v83cZRQMxBQi",
        "colab_type": "text"
      },
      "source": [
        "## Load Existing Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acaXlXpUxDxM",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "81b6e3ea-1712-475d-a701-59e3f6c8433e"
      },
      "source": [
        "load = True #@param {type:\"boolean\"}\n",
        "\n",
        "if load:\n",
        "  # Returns a compiled model identical to the previous one\n",
        "  !curl -O https://selbystorage.s3-us-west-2.amazonaws.com/research/office_3/model_intensity.h5\n",
        "  !mv model_intensity.h5 model/\n",
        "  model_path_full = model_path + '/' + 'model_intensity.h5'\n",
        "  model = load_model(model_path_full)\n",
        "  print(\"Loaded previous model: {} \\n\".format(model_path_full))\n",
        "else:\n",
        "  print(\"No previous model loaded \\n\")"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 8297k  100 8297k    0     0  10.4M      0 --:--:-- --:--:-- --:--:-- 10.4M\n",
            "Loaded previous model: ./model/model_intensity.h5 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "71O-pWk9AQy3",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## Train the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nkossmrAUo2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "053e5bbf-bda3-4da7-f802-e1cfe0d6e92c"
      },
      "source": [
        "# Define step sizes\n",
        "STEP_SIZE_TRAIN = len(train_samples) / batch_size_value\n",
        "STEP_SIZE_VALID = len(validation_samples) / batch_size_value\n",
        "\n",
        "# Define number of epochs\n",
        "n_epoch = 50\n",
        "\n",
        "# Define callbacks\n",
        "# callbacks_list = [TensorBoardColabCallback(tbc)]\n",
        "# callbacks_list = [TensorBoardColabCallback(tbc), early_stop]\n",
        "# callbacks_list = [TensorBoardColabCallback(tbc), early_stop, checkpoint]\n",
        "callbacks_list = [TensorBoardColabCallback(tbc), early_stop, checkpoint, reduce_lr]\n",
        "\n",
        "# Fit the model\n",
        "history_object = model.fit_generator(\n",
        "    generator=train_generator,\n",
        "    steps_per_epoch=STEP_SIZE_TRAIN,\n",
        "    validation_data=validation_generator,\n",
        "    validation_steps=STEP_SIZE_VALID,\n",
        "    callbacks=callbacks_list,\n",
        "    use_multiprocessing=True,\n",
        "    epochs=n_epoch)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "154/154 [==============================] - 87s 563ms/step - loss: 1.6989 - mean_squared_error: 1.6989 - mean_absolute_error: 1.2303 - mean_absolute_percentage_error: 2537433.2419 - cosine_proximity: -0.0099 - val_loss: 1.7245 - val_mean_squared_error: 1.7245 - val_mean_absolute_error: 1.2423 - val_mean_absolute_percentage_error: 359371.0461 - val_cosine_proximity: 0.6513\n",
            "\n",
            "Epoch 00001: val_loss did not improve from 0.25825\n",
            "Epoch 2/50\n",
            "154/154 [==============================] - 84s 544ms/step - loss: 1.6901 - mean_squared_error: 1.6901 - mean_absolute_error: 1.2264 - mean_absolute_percentage_error: 1111287.3951 - cosine_proximity: 0.0278 - val_loss: 1.7239 - val_mean_squared_error: 1.7239 - val_mean_absolute_error: 1.2447 - val_mean_absolute_percentage_error: 135715.2303 - val_cosine_proximity: 0.6531\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 0.25825\n",
            "Epoch 3/50\n",
            "154/154 [==============================] - 84s 547ms/step - loss: 1.6869 - mean_squared_error: 1.6869 - mean_absolute_error: 1.2249 - mean_absolute_percentage_error: 1008224.7047 - cosine_proximity: -0.0091 - val_loss: 1.7412 - val_mean_squared_error: 1.7412 - val_mean_absolute_error: 1.2491 - val_mean_absolute_percentage_error: 829392.7223 - val_cosine_proximity: 0.6490\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.25825\n",
            "Epoch 4/50\n",
            "154/154 [==============================] - 84s 547ms/step - loss: 1.6889 - mean_squared_error: 1.6889 - mean_absolute_error: 1.2260 - mean_absolute_percentage_error: 1412263.8743 - cosine_proximity: -0.0113 - val_loss: 1.7407 - val_mean_squared_error: 1.7407 - val_mean_absolute_error: 1.2500 - val_mean_absolute_percentage_error: 764262.0272 - val_cosine_proximity: 0.6540\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 0.25825\n",
            "Epoch 5/50\n",
            "154/154 [==============================] - 84s 546ms/step - loss: 1.6907 - mean_squared_error: 1.6907 - mean_absolute_error: 1.2266 - mean_absolute_percentage_error: 828353.1170 - cosine_proximity: -0.0121 - val_loss: 1.7345 - val_mean_squared_error: 1.7345 - val_mean_absolute_error: 1.2476 - val_mean_absolute_percentage_error: 642487.7258 - val_cosine_proximity: 0.6440\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.25825\n",
            "Epoch 6/50\n",
            "154/154 [==============================] - 84s 547ms/step - loss: 1.6900 - mean_squared_error: 1.6900 - mean_absolute_error: 1.2263 - mean_absolute_percentage_error: 477814.4326 - cosine_proximity: -0.0047 - val_loss: 1.7299 - val_mean_squared_error: 1.7299 - val_mean_absolute_error: 1.2463 - val_mean_absolute_percentage_error: 307544.4697 - val_cosine_proximity: 0.6482\n",
            "\n",
            "Epoch 00006: val_loss did not improve from 0.25825\n",
            "Epoch 7/50\n",
            "154/154 [==============================] - 84s 545ms/step - loss: 1.6886 - mean_squared_error: 1.6886 - mean_absolute_error: 1.2257 - mean_absolute_percentage_error: 516291.1301 - cosine_proximity: 0.0073 - val_loss: 1.7157 - val_mean_squared_error: 1.7157 - val_mean_absolute_error: 1.2402 - val_mean_absolute_percentage_error: 33908.3636 - val_cosine_proximity: -0.6482\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.25825\n",
            "Epoch 8/50\n",
            "154/154 [==============================] - 84s 547ms/step - loss: 1.6905 - mean_squared_error: 1.6905 - mean_absolute_error: 1.2262 - mean_absolute_percentage_error: 562722.2738 - cosine_proximity: 0.0023 - val_loss: 1.7237 - val_mean_squared_error: 1.7237 - val_mean_absolute_error: 1.2433 - val_mean_absolute_percentage_error: 251274.9909 - val_cosine_proximity: 0.6474\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 0.25825\n",
            "Epoch 9/50\n",
            "  8/154 [>.............................] - ETA: 56s - loss: 1.7026 - mean_squared_error: 1.7026 - mean_absolute_error: 1.2320 - mean_absolute_percentage_error: 139298.7649 - cosine_proximity: 0.0745"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Process PoolWorker-5:\n",
            "Traceback (most recent call last):\n",
            "Process PoolWorker-6:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python2.7/multiprocessing/process.py\", line 267, in _bootstrap\n",
            "  File \"/usr/lib/python2.7/multiprocessing/process.py\", line 267, in _bootstrap\n",
            "    self.run()\n",
            "  File \"/usr/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/usr/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
            "    self.run()\n",
            "    task = get()\n",
            "  File \"/usr/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
            "  File \"/usr/lib/python2.7/multiprocessing/queues.py\", line 376, in get\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/usr/lib/python2.7/multiprocessing/pool.py\", line 113, in worker\n",
            "    result = (True, func(*args, **kwds))\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras/utils/data_utils.py\", line 626, in next_sample\n",
            "    return recv()\n",
            "KeyboardInterrupt\n",
            "    return six.next(_SHARED_SEQUENCES[uid])\n",
            "  File \"<ipython-input-51-c128e581afab>\", line 27, in generator\n",
            "    batch = it.next()\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras_preprocessing/image/iterator.py\", line 116, in next\n",
            "    return self._get_batches_of_transformed_samples(index_array)\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras_preprocessing/image/numpy_array_iterator.py\", line 153, in _get_batches_of_transformed_samples\n",
            "    x.astype(self.dtype), params)\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras_preprocessing/image/image_data_generator.py\", line 870, in apply_transform\n",
            "    order=self.interpolation_order)\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras_preprocessing/image/affine_transformations.py\", line 333, in apply_affine_transform\n",
            "    cval=cval) for x_channel in x]\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/scipy/ndimage/interpolation.py\", line 480, in affine_transform\n",
            "    output, order, mode, cval, None, None)\n",
            "KeyboardInterrupt\n",
            "Process PoolWorker-7:\n",
            "Process PoolWorker-8:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python2.7/multiprocessing/process.py\", line 267, in _bootstrap\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/lib/python2.7/multiprocessing/process.py\", line 267, in _bootstrap\n",
            "    self.run()\n",
            "    self.run()\n",
            "  File \"/usr/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "  File \"/usr/lib/python2.7/multiprocessing/pool.py\", line 102, in worker\n",
            "    task = get()\n",
            "  File \"/usr/lib/python2.7/multiprocessing/queues.py\", line 376, in get\n",
            "  File \"/usr/lib/python2.7/multiprocessing/process.py\", line 114, in run\n",
            "    return recv()\n",
            "    self._target(*self._args, **self._kwargs)\n",
            "KeyboardInterrupt\n",
            "  File \"/usr/lib/python2.7/multiprocessing/pool.py\", line 113, in worker\n",
            "    result = (True, func(*args, **kwds))\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras/utils/data_utils.py\", line 626, in next_sample\n",
            "    return six.next(_SHARED_SEQUENCES[uid])\n",
            "  File \"<ipython-input-51-c128e581afab>\", line 27, in generator\n",
            "    batch = it.next()\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras_preprocessing/image/iterator.py\", line 116, in next\n",
            "    return self._get_batches_of_transformed_samples(index_array)\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras_preprocessing/image/numpy_array_iterator.py\", line 153, in _get_batches_of_transformed_samples\n",
            "    x.astype(self.dtype), params)\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras_preprocessing/image/image_data_generator.py\", line 870, in apply_transform\n",
            "    order=self.interpolation_order)\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/keras_preprocessing/image/affine_transformations.py\", line 333, in apply_affine_transform\n",
            "    cval=cval) for x_channel in x]\n",
            "  File \"/usr/local/lib/python2.7/dist-packages/scipy/ndimage/interpolation.py\", line 480, in affine_transform\n",
            "    output, order, mode, cval, None, None)\n",
            "KeyboardInterrupt\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OdaGfWNxBT4T",
        "colab_type": "text"
      },
      "source": [
        "## Save the Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWE--9r4BZEk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save model\n",
        "model_path_full = model_path + '/'\n",
        "\n",
        "model.save(model_path_full + 'model.h5')\n",
        "with open(model_path_full + 'model.json', 'w') as output_json:\n",
        "    output_json.write(model.to_json())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y-pCmYO1A89_",
        "colab_type": "text"
      },
      "source": [
        "# Evaluate the Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O7o-6SbBD9zx",
        "colab_type": "text"
      },
      "source": [
        "## Plot the Training Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-M9fEvWD_ZJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "bb36ff71-e182-4b68-dd35-9ed4a7c94507"
      },
      "source": [
        "# Plot the training and validation loss for each epoch\n",
        "print('Generating loss chart...')\n",
        "plt.plot(history_object.history['loss'])\n",
        "plt.plot(history_object.history['val_loss'])\n",
        "plt.title('model mean squared error loss')\n",
        "plt.ylabel('mean squared error loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['training set', 'validation set'], loc='upper right')\n",
        "plt.savefig(model_path + '/model.png')\n",
        "\n",
        "# Done\n",
        "print('Done.')"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Generating loss chart...\n",
            "Done.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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M68NnG/bZHdmMMe1CIElhuYg8LSLj3eEpIKDrFFT1X6p6qqqeoqr3u9PuUdXZ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\nMT6WFIwxxvj8fzmWurmFzx3gAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fl5JppdwCRWD",
        "colab_type": "text"
      },
      "source": [
        "## Print Performance Metrics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "podNBS9cBRNW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "1b6d47d9-e08c-4a9f-84d6-cf5a6cac5b41"
      },
      "source": [
        "scores = model.evaluate_generator(validation_generator, STEP_SIZE_VALID, use_multiprocessing=True)\n",
        "\n",
        "metrics_names = model.metrics_names\n",
        "\n",
        "for i in range(len(model.metrics_names)):\n",
        "  print(\"Metric: {} - {}\".format(metrics_names[i],scores[i]))\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Metric: loss - 0.190782873646\n",
            "Metric: mean_squared_error - 0.190782873646\n",
            "Metric: mean_absolute_error - 0.330186681136\n",
            "Metric: mean_absolute_percentage_error - 28329829.3002\n",
            "Metric: cosine_proximity - -0.870888157895\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OHL1otavPhX0",
        "colab_type": "text"
      },
      "source": [
        "## Compute Prediction Statistics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xlv7PUnPeSYd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "outputId": "b13707af-52ff-47a6-f35e-a63593845188"
      },
      "source": [
        "# Define image loading function\n",
        "def load_images(dataframe):\n",
        "  \n",
        "  # initialize images array\n",
        "  images = []\n",
        "  \n",
        "  for i in dataframe.index.values:\n",
        "    name = data_set + '/' + dataframe.loc[i,'filename']\n",
        "    center_image = cv2.imread(name)\n",
        "    center_image = cv2.resize(center_image, (320,180))\n",
        "    images.append(center_image)\n",
        "    \n",
        "  return np.array(images)\n",
        "  \n",
        "# Load images \n",
        "test_size = 200\n",
        "df_test = df.sample(frac=1).reset_index(drop=True)\n",
        "df_test = df_test.head(test_size)\n",
        "\n",
        "test_images = load_images(df_test)\n",
        "\n",
        "batch_size = 32\n",
        "preds = model.predict(test_images, batch_size=batch_size, verbose=1)\n",
        "\n",
        "#print(\"Preds: {} \\n\".format(preds))\n",
        "\n",
        "testY = df_test.iloc[:,4].values\n",
        "\n",
        "#print(\"Labels: {} \\n\".format(testY))\n",
        "\n",
        "df_testY = pd.Series(testY)\n",
        "df_preds = pd.Series(preds.flatten)\n",
        "\n",
        "# Replace 0 angle values\n",
        "if df_testY.eq(0).any():\n",
        "  df_testY.replace(0, 0.0001,inplace=True)\n",
        "\n",
        "# Calculate the difference\n",
        "diff = preds.flatten() - df_testY\n",
        "percentDiff = (diff / testY) * 100\n",
        "absPercentDiff = np.abs(percentDiff)\n",
        "\n",
        "# compute the mean and standard deviation of the absolute percentage\n",
        "# difference\n",
        "mean = np.mean(absPercentDiff)\n",
        "std = np.std(absPercentDiff)\n",
        "print(\"[INFO] mean: {:.2f}%, std: {:.2f}%\".format(mean, std))\n",
        "\n",
        "# Compute the mean and standard deviation of the difference\n",
        "print(diff.describe())\n",
        "\n",
        "# Plot a histogram of the prediction errors\n",
        "num_bins = 25\n",
        "hist, bins = np.histogram(diff, num_bins)\n",
        "center = (bins[:-1]+ bins[1:]) * 0.5\n",
        "plt.bar(center, hist, width=0.05)\n",
        "plt.title('Historgram of Predicted Error')\n",
        "plt.xlabel('Steering Angle')\n",
        "plt.ylabel('Number of predictions')\n",
        "plt.xlim(-2.0, 2.0)\n",
        "plt.plot(np.min(diff), np.max(diff))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "200/200 [==============================] - 0s 381us/step\n",
            "[INFO] mean: 22781.22%, std: 143750.05%\n",
            "count    200.000000\n",
            "mean      -0.069993\n",
            "std        0.367974\n",
            "min       -0.937922\n",
            "25%       -0.275053\n",
            "50%       -0.170840\n",
            "75%       -0.080705\n",
            "max        1.389061\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5746de2690>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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k+T8CpxTdgaRhkv4g6eo0v6OkWyXdJ+kSSRu2GrSZmZWnSGIYHRGXAs8DRMRa4LkW9nEy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\nM8txYjAzs5z/D4GbVgjzYporAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyalDh4uIvIT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "967dd737-a366-4b55-ccef-b8b4eb279949"
      },
      "source": [
        "# Plot a Scatter Plot of the Error\n",
        "plt.scatter(testY, preds)\n",
        "plt.xlabel('True Values ')\n",
        "plt.ylabel('Predictions ')\n",
        "plt.axis('equal')\n",
        "plt.axis('square')\n",
        "plt.xlim([-1.75,1.75])\n",
        "plt.ylim([-1.75,1.75])\n",
        "plt.plot([-1.75, 1.75], [-1.75, 1.75], color='k', linestyle='-', linewidth=.1)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f5746c8d910>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ynQussbAlskV1z13R5nE28EQC541rJfDhsDdpDvByWfU1VZIOLrVzSZpNkAlJ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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5jVE6WfNGDEM",
        "colab_type": "text"
      },
      "source": [
        "## Plot a Prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GSJ08rg3QDP7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "1479a65f-e7e2-47d7-c6bd-222a7d14978d"
      },
      "source": [
        "# Plot the image with the actual and predicted steering angle\n",
        "index = random.randint(0,df_test.shape[0]-1)\n",
        "img_name = data_set + '/' + df_test.loc[index,'filename']\n",
        "center_image = cv2.imread(img_name)\n",
        "center_image = cv2.cvtColor(center_image,cv2.COLOR_RGB2BGR)\n",
        "center_image_mod = cv2.resize(center_image, (320,180)) \n",
        "plt.imshow(center_image_mod)\n",
        "plt.grid(False)\n",
        "plt.xlabel('Actual: {:.2f} Predicted: {:.2f}'.format(df_test.loc[index,'angle'],float(preds[index])))\n",
        "plt.show() \n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BojvZPQH0ACRZw3CNA+T++++Hpmn4kz/5EwnxVKDv6+tDKpXC8vIylpaW0Nvb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3+/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\ni3wAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nw3xUyrWU0_Q",
        "colab_type": "text"
      },
      "source": [
        "#Visualize the Network\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDmn0c0AU3w_",
        "colab_type": "text"
      },
      "source": [
        "##Show the Model Summary"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8VTkX6ofU3Ko",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KsDr5kvMVfut",
        "colab_type": "text"
      },
      "source": [
        "##Access Individual Layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4b70eI0mVnH3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creating a mapping of layer name ot layer details \n",
        "# We will create a dictionary layers_info which maps a layer name to its charcteristics\n",
        "layers_info = {}\n",
        "for i in model.layers:\n",
        "    layers_info[i.name] = i.get_config()\n",
        "\n",
        "# Here the layer_weights dictionary will map every layer_name to its corresponding weights\n",
        "layer_weights = {}\n",
        "for i in model.layers:\n",
        "    layer_weights[i.name] = i.get_weights()\n",
        "\n",
        "pprint.pprint(layers_info['conv_5'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrqmP6unW99W",
        "colab_type": "text"
      },
      "source": [
        "##Visualize the Filters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lgACkEzLXAw3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Visualize the first filter of each convolution layer\n",
        "layers = model.layers\n",
        "layer_ids = [2,3,4,6,7]\n",
        "\n",
        "#plot the filters\n",
        "fig,ax = plt.subplots(nrows=1,ncols=5)\n",
        "for i in range(5):\n",
        "    ax[i].imshow(layers[layer_ids[i]].get_weights()[0][:,:,:,0][:,:,0],cmap='gray')\n",
        "    ax[i].set_title('Conv'+str(i+1))\n",
        "    ax[i].set_xticks([])\n",
        "    ax[i].set_yticks([])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1SFhmyiuh29",
        "colab_type": "text"
      },
      "source": [
        "##Visualize the Saliency Map\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQmQVfJoVfTK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "outputId": "3552c100-9bc2-4812-a3ae-b528fea851bc"
      },
      "source": [
        "!pip install -I scipy==1.2.*\n",
        "!pip install git+https://github.com/raghakot/keras-vis.git -U\n",
        "\n",
        "# import specific functions from keras-vis package\n",
        "from vis.utils import utils\n",
        "from vis.visualization import visualize_saliency, visualize_cam, overlay"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting scipy==1.2.*\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1d/f6/7c16d60aeb3694e5611976cb4f1eaf1c6b7f1e7c55771d691013405a02ea/scipy-1.2.2-cp27-cp27mu-manylinux1_x86_64.whl (24.8MB)\n",
            "\u001b[K     |████████████████████████████████| 24.8MB 126kB/s \n",
            "\u001b[?25hCollecting numpy>=1.8.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/3a/5f/47e578b3ae79e2624e205445ab77a1848acdaa2929a00eeef6b16eaaeb20/numpy-1.16.6-cp27-cp27mu-manylinux1_x86_64.whl (17.0MB)\n",
            "\u001b[K     |████████████████████████████████| 17.0MB 222kB/s \n",
            "\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorflow-estimator==1.15.1, but you'll have tensorflow-estimator 1.15.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: fastai 0.7.0 has requirement torch<0.4, but you'll have torch 1.3.1+cu100 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: numpy, scipy\n",
            "Successfully installed numpy-1.16.6 scipy-1.2.2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy",
                  "scipy"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/raghakot/keras-vis.git\n",
            "  Cloning https://github.com/raghakot/keras-vis.git to /tmp/pip-req-build-mXIKbH\n",
            "  Running command git clone -q https://github.com/raghakot/keras-vis.git /tmp/pip-req-build-mXIKbH\n",
            "Requirement already satisfied, skipping upgrade: keras>=2.0 in /usr/local/lib/python2.7/dist-packages (from keras-vis==0.5.0) (2.2.4)\n",
            "Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python2.7/dist-packages (from keras-vis==0.5.0) (1.12.0)\n",
            "Requirement already satisfied, skipping upgrade: scikit-image in /usr/local/lib/python2.7/dist-packages (from keras-vis==0.5.0) (0.14.3)\n",
            "Requirement already satisfied, skipping upgrade: matplotlib in /usr/local/lib/python2.7/dist-packages (from keras-vis==0.5.0) (2.2.4)\n",
            "Requirement already satisfied, skipping upgrade: h5py in /usr/local/lib/python2.7/dist-packages (from keras-vis==0.5.0) (2.8.0)\n",
            "Requirement already satisfied, skipping upgrade: keras-applications>=1.0.6 in /usr/local/lib/python2.7/dist-packages (from keras>=2.0->keras-vis==0.5.0) (1.0.8)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.9.1 in /usr/local/lib/python2.7/dist-packages (from keras>=2.0->keras-vis==0.5.0) (1.16.6)\n",
            "Requirement already satisfied, skipping upgrade: scipy>=0.14 in /usr/local/lib/python2.7/dist-packages (from keras>=2.0->keras-vis==0.5.0) (1.2.2)\n",
            "Requirement already satisfied, skipping upgrade: keras-preprocessing>=1.0.5 in /usr/local/lib/python2.7/dist-packages (from keras>=2.0->keras-vis==0.5.0) (1.1.0)\n",
            "Requirement already satisfied, skipping upgrade: pyyaml in /usr/local/lib/python2.7/dist-packages (from keras>=2.0->keras-vis==0.5.0) (3.13)\n",
            "Requirement already satisfied, skipping upgrade: PyWavelets>=0.4.0 in /usr/local/lib/python2.7/dist-packages (from scikit-image->keras-vis==0.5.0) (1.0.3)\n",
            "Requirement already satisfied, skipping upgrade: cloudpickle>=0.2.1 in /usr/local/lib/python2.7/dist-packages (from scikit-image->keras-vis==0.5.0) (0.6.1)\n",
            "Requirement already satisfied, skipping upgrade: networkx>=1.8 in /usr/local/lib/python2.7/dist-packages (from scikit-image->keras-vis==0.5.0) (2.2)\n",
            "Requirement already satisfied, skipping upgrade: pillow>=4.3.0 in /usr/local/lib/python2.7/dist-packages (from scikit-image->keras-vis==0.5.0) (4.3.0)\n",
            "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python2.7/dist-packages (from matplotlib->keras-vis==0.5.0) (0.10.0)\n",
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            "Requirement already satisfied, skipping upgrade: olefile in /usr/local/lib/python2.7/dist-packages (from pillow>=4.3.0->scikit-image->keras-vis==0.5.0) (0.46)\n",
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            "Building wheels for collected packages: keras-vis\n",
            "  Building wheel for keras-vis (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-vis: filename=keras_vis-0.5.0-py2.py3-none-any.whl size=38887 sha256=133fa4adce41ffb9168829b645bce2d2b33ad6795a9e36e99b49a49db5340c54\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-vfftza/wheels/c5/ae/e7/b34d1cb48b1898f606a5cce08ebc9521fa0588f37f1e590d9f\n",
            "Successfully built keras-vis\n",
            "Installing collected packages: keras-vis\n",
            "  Found existing installation: keras-vis 0.4.1\n",
            "    Uninstalling keras-vis-0.4.1:\n",
            "      Successfully uninstalled keras-vis-0.4.1\n",
            "Successfully installed keras-vis-0.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cWwL58SWup5M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# View a Single Image \n",
        "index = random.randint(0,df.shape[0]-1)\n",
        "img_name = data_set + '/' + df.loc[index,'filename']\n",
        "\n",
        "sample_image = cv2.imread(img_name)\n",
        "sample_image = cv2.cvtColor(sample_image,cv2.COLOR_RGB2BGR)\n",
        "sample_image_mod = cv2.resize(sample_image, (320,180))\n",
        "plt.imshow(sample_image_mod)\n",
        " \n",
        "layer_idx = utils.find_layer_idx(model, 'conv_5')\n",
        "\n",
        "grads = visualize_saliency(model, \n",
        "                           layer_idx, \n",
        "                           filter_indices=None, \n",
        "                           seed_input=sample_image_mod,\n",
        "                           grad_modifier='absolute',\n",
        "                           backprop_modifier='guided')\n",
        "\n",
        "plt.imshow(grads, alpha = 0.6)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QMGEoYbVHnaJ",
        "colab_type": "text"
      },
      "source": [
        "# References:\n",
        "[Keras, Regression, and CNNs](https://www.pyimagesearch.com/2019/01/28/keras-regression-and-cnns/)\n",
        "\n",
        "[Regression with Keras](https://www.pyimagesearch.com/2019/01/21/regression-with-keras/)\n",
        "\n",
        "[How to use Keras fit and fit_generator](https://www.pyimagesearch.com/2018/12/24/how-to-use-keras-fit-and-fit_generator-a-hands-on-tutorial/)\n",
        "\n",
        "[Image Classification with Convolutional Neural Networks](https://colab.research.google.com/github/tensorflow/examples/blob/master/courses/udacity_intro_to_tensorflow_for_deep_learning/l04c01_image_classification_with_cnns.ipynb#scrollTo=7MqDQO0KCaWS)\n",
        "\n",
        "[Keras Image Processing Documentation](https://keras.io/preprocessing/image/)\n",
        "\n",
        "[Attribution.ipynb](https://colab.research.google.com/github/idealo/cnn-exposed/blob/master/notebooks/Attribution.ipynb#scrollTo=jqSOW0pQniCw)\n",
        "\n",
        "[A Guide to Understanding Convolutional Neural Networks (CNNs) using Visualization](https://www.analyticsvidhya.com/blog/2019/05/understanding-visualizing-neural-networks/)\n",
        "\n",
        "[Visualizing attention on self driving car](https://github.com/raghakot/keras-vis/blob/master/applications/self_driving/visualize_attention.ipynb)\n",
        "\n",
        "[Exploring Image Data Augmentation with Keras and Tensorflow](https://towardsdatascience.com/exploring-image-data-augmentation-with-eras-and-tensorflow-a8162d89b844\n",
        ")\n",
        "\n",
        "[Tensorboard Documentation](https://keras.io/callbacks/#tensorboard)"
      ]
    }
  ]
}